This invention is in the field of oil and natural gas production, and is more specifically directed to reservoir management and well management in such production.
Current economic factors in the oil and gas industry have raised the stakes for the optimization of hydrocarbon production. On one side of the equation, the market prices of oil and natural gas have reached new highs, by historical standards. However, the costs of drilling of new wells and operating existing wells are also high by historical standards, because of the extreme depths to which new producing wells must be drilled, because of the increased costs of the technology utilized, and because of other physical barriers to exploiting reservoirs. These higher economic stakes require production operators to devote substantial resources toward gathering and analyzing measurements from existing hydrocarbon wells and reservoirs in the management of production fields and of individual wells within a given field.
For example, the optimization of production from a given field or reservoir involves decisions regarding the number and placement of wells, including whether to add or shut-in wells. Secondary and tertiary recovery operations, for example involving the injection of water or gas into the reservoir, require decisions regarding whether to initiate or cease such operations, and also how many wells are to serve as injection wells and their locations in the field. Some wells may require well treatment, such as fracturing of the wellbore if drilling and production activity has packed the wellbore surface sufficiently to slow or stop production. In some cases, production may be improved by shutting-in one or more wells; in other situations, a well may have to be shut-in for an extended period of time, in which case optimization of production may require a reconfiguration of the production field. As evident from these examples, the optimization of a production field is a complex problem, involving many variables and presenting many choices.
The complexity of this problem is exacerbated by the scale of modern large oil and gas production fields, which often include hundreds of wells and a complex network of surface lines that interconnect these wells with centralized transportation or processing facilities. These activities and operations are made significantly more complex by variations in well maturity over a large number of wells in the production field, in combination with finite secondary and tertiary recovery resources. As such, the decisions for optimum production and economic return become extremely complex, especially for complex fields. Additionally, there may be added challenges in the later life operation of the production field. In addition, as mentioned above, the economic stakes are high.
In recent years, advances have been made in improving the measurement and analysis of parameters involved in oil and gas production, with the goal of improving production decisions. For example, surface pressure gauges and flow meters deployed at the wellhead. Further, the surface lines interconnecting wellheads with centralized processing facilities, are now commonly monitored. These gauges and meters are also used with separating equipment, to measure the flow of each phase (oil, gas, water). Because these sensors can provide data on virtually a continuous basis, an overwhelming quantity of measurement data can rapidly be obtained from a modern complex production field. This vast amount of data, along with the complexity of the production field, and the difficulty in deriving a manageable model of the reservoir and the production field, add up to create a very complex and difficult optimization problem for the reservoir engineering staff.
One approach to managing production optimization for a complex production field is described in U.S. Pat. No. 6,236,894, incorporated herein by this reference. This approach uses an adaptive network, specifically involving genetic algorithms, to derive well operation parameters for optimizing production. The U.S. Pat. No. 6,236,894 illustrates the nature and complexity some aspects and problems associated with optimization of a modern production field.
By way of further background, it is known that incremental fluid flow from a well is approximately proportional to the difference in pressure between the reservoir pressure and the pressure in the production tubing at the reservoir depth. This pressure may be generally considered as the sum of the production header pressure at the wellhead plus the combination of the static head within the well and the frictional losses along the wellbore to the surface. This important relationship between reservoir pressure and flow rate is the basis of conventional well testing, which is useful in both analyzing the performance of a specific well, and also in determining reservoir-wide parameters, such as reservoir pressure.
Typically, pressure transient well tests involve the characterization of the bottomhole pressure relative to the flow rate, to derive such parameters as reservoir pressure, permeability of the surrounding reservoir formation, and the “skin” of the borehole. These parameters are useful in understanding the performance of a given well. These pressure transient tests can be classified as “shut-in” (or “build-up”) tests, on one hand, or as “drawdown” tests, on the other. In the shut-in test, the downhole pressure is measured over time, beginning prior to shutting-in the well and continuing after shut-in. The reservoir pressure is determined from the measurement of the downhole pressure at such time as the time-rate-of-change of pressure stabilizes, following the shut-in event. Conversely, a well can be characterized in a drawdown test, which is the opposite of a shut-in test in that the flow is measured before, during, and after a dramatic increase in well flow, such as opening the choke from a shut-in condition.
It has been observed that, for determination of reservoir pressure from these conventional pressure transient tests, the duration of the shut-in event required to achieve the steady-state ranges from hours to as long as days, depending on the characteristics of the reservoir. The loss of production during the shut-in period discourages frequent pressure transient well tests, and thus raises the cost of acquiring the data necessary for determining reservoir pressure, permeability, skin factor, and other well and reservoir characterization parameters.
Recent years have brought the development of reliable downhole pressure sensors that can be plumbed into the production string and left in the wellbore during production. The improved reliability of these sensors over time at elevated wellbore temperatures and pressures, has resulted in the increasing popularity of real-time downhole pressure sensors to continuously monitor downhole pressure during production at one or more wellbore depths in each well of a production field. These downhole sensors are typically used for monitoring and managing the individual wells, on a day-to-day basis.
The widespread deployment of these continuous-time downhole sensors in a production field rapidly generates a huge volume of data, especially considering that typical measurement frequencies are on the order of one measurement per second per sensor. While each shut-in of a producing well, planned or unplanned, provides an opportunity to perform pressure transient analysis, the volume of data and the tedious manual process required of the reservoir engineer to extract meaningful information such as reservoir pressure is often prohibitive. This tedious work process involves using unlinked computer applications to visually inspect the massive amount of downhole pressure measurement data, identify the build-up and its associated pressure and rate data, extract, filter, and format that data, and then perform the analysis itself. It is a massive task for the reservoir engineer simply to determine which data are important in analyzing the reservoir. In addition, meaningful analysis requires the reservoir engineer to locate, extract, filter, and correlate the data from wells over the entire production field, in order to draw accurate conclusions. It has been observed, in connection with this invention, that the time and effort required to perform this data analysis using conventional techniques reduces the frequency and timeliness of such analysis. In addition, the identification of the build-up and draw-down events is a somewhat subjective determination on the part of the petroleum engineer, reservoir engineer, geologist, operator, technician, or any other human user, rendering the analysis prone to inconsistencies and errors. These factors all limit the frequency and accuracy of reservoir pressure analysis performed in this conventional manner, and can lead to erroneous well and reservoir decisions caused by inaccurate and out-of-date information.
By way of further background, the automated gathering and filtering of downhole and surface pressure and flow measurements, in order to reduce the engineering effort required to analyze measurements by permanent downhole gauges during production, is known. According to one known report on such an automation effort, a zero flow rate over a measurement time period is detected as a shut-in period, and is analyzed as a “build-up” or shut-in well test according to an automated non-linear regression analysis.